> For the complete documentation index, see [llms.txt](https://docs.apryse.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.apryse.com/explore-docs-with-ai/access-ai-integration-options.md).

# Access AI integration options

Discover different AI-ready options for accessing Apryse SDK documentation.

Our documentation provides multiple AI integration options, ranging from conversational experiences within the documentation site to AI-readable content formats and Model Context Protocol (MCP) integrations.

Most AI features can be accessed directly from the **Ask** dropdown on any documentation page. The following table describes each option.

<table data-search="false"><thead><tr><th>AI integration option</th><th width="374.90625">Description</th></tr></thead><tbody><tr><td><strong>AI Assistant</strong></td><td>Open the AI assistant and find answers about the current page.</td></tr><tr><td><strong>Copy page</strong></td><td>Copy the page as Markdown so you can paste it as context in your LLMs.</td></tr><tr><td><strong>View as Markdown</strong></td><td>Display the page as plain text or use the Markdown endpoint to allow AI to fetch the content.</td></tr><tr><td><strong>Open in ChatGPT or Claude</strong></td><td>Open a new chat in each AI assistant, prefilled with a prompt that references the current documentation page. You can then ask ChatGPT or Claude questions about the page's content.  </td></tr><tr><td><strong>Connect with MCP</strong></td><td>Copy the Apryse docs MCP server URL to add it to a client's MCP configuration.</td></tr><tr><td><strong>Connect to VS Code</strong></td><td>Open VS Code and prepopulate the Apryse docs MCP server configuration for installation.</td></tr><tr><td><strong>Connect to Claude Code or Codex</strong></td><td>Copy the Apryse docs MCP server URL to add it to the client's MCP configuration.</td></tr><tr><td><strong>Export as PDF</strong></td><td>Export the page content as a PDF.</td></tr></tbody></table>

We also expose `llms.txt` and `llms-full.txt` endpoints to make it easier for AI systems to discover and consume documentation. For more, see [Access llms.txt](/explore-docs-with-ai/access-llms.txt.md) and [Access llms-full.txt](/explore-docs-with-ai/access-llms-full.txt.md).

### Next steps

Explore the following topics to find the AI interaction option that best fits your use case:

* [Chat with AI Assistant](/explore-docs-with-ai/chat-with-ai-assistant.md)
* [Access docs as Markdown](/explore-docs-with-ai/access-docs-as-markdown.md)
* [Access llms.txt](/explore-docs-with-ai/access-llms.txt.md)
* [Access llms-full.txt](/explore-docs-with-ai/access-llms-full.txt.md)
* [Connect to MCP](/explore-docs-with-ai/connect-to-mcp.md)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.apryse.com/explore-docs-with-ai/access-ai-integration-options.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.